Reduced Financial Ops Time by 52% with an AI Finance Co-pilot for Saudi-Based FinTech Startup

The startup’s early users were SME owners who juggled operations, sales, and finance with minimal back-office support. Most of them relied on basic accounting tools and manual transaction categorization. They struggled to understand where money was going, how much runway they had, and when to expect cash crunches.

The founders wanted an AI-powered MVP that would sit on top of existing accounting and banking connections to give intelligent insights, not raw numerics. The translation was critical to make SME owners understand it and act proactively.

• Fragmented financial data across bank feeds, accounting tools, and invoicing systems.
• Lack of financial literacy among some SME users made traditional dashboards ineffective.
• High volume of low-value manual work, such as categorizing expenses and identifying recurring patterns.
• The need to build trust around AI-driven recommendations in a domain where accuracy and reliability are critical.

• We started with an AI audit of the existing data connections and user workflows. We worked with the founding team to define a narrow yet powerful set of use cases for the MVP: automated categorization, cash flow snapshots, and plain-language financial insights.
• We designed and developed an AI-powered finance co-pilot that was connected to existing accounting and banking systems through secure APIs and normalized transaction data in PostgreSQL.
• Our AI engineers used LangChain and OpenAI models to categorize transactions, detect patterns, and generate natural language summaries.
• The team also empowered the client with real-time recommendations in a clean React.js dashboard. This includes alerts such as unusual spending, delayed receivables, or opportunities to renegotiate vendor terms.
• Python and FastAPI were used to power-up the AI functionalities and intelligent analytics. Kafka was used to handle streaming transaction events and perform real-time recalculations. All AI workloads were deployed using Docker within a secure cloud environment with role based access.

The delivered AI-powered FinTech MVP repositioned the FinTech startup from a basic financial tool provider to a proactive financial intelligence partner.

• 89% faster completion of month end closing tasks for SMEs that adopted the co-pilot.
• 54% reduction in manual transaction categorization, freeing teams to focus on strategic decisions rather than data cleanup.
• 41% improvement in cashflow forecast accuracy compared to previous manual spreadsheets. It helped SMEs anticipate crunch periods confidently.
• 3.1x increase in the number of financial insights viewed and acted upon by SME users. It is tracked through in-app engagement metrics.